Inferring Brain Signals Synchronicity from a Sample of EEG Readings

TL;DR

This paper presents a new methodological framework for inferring synchronized brain activity patterns from heterogeneous EEG samples, combining classical statistical techniques with machine learning and Bayesian methods.

Contribution

It introduces a novel approach that addresses the challenges of pooling EEG data across subjects for detecting recurrent brain activation patterns.

Findings

Proposes a Bayesian and machine learning-based method for EEG analysis.

Reframes the inferential problem using time-series, clustering, and functional data analysis.

Addresses computational challenges in analyzing heterogeneous EEG data.

Abstract

Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms (EEG) is scientifically and methodologically challenging. While it is intuitively and statistically appealing to rely on readings from more than one individual in order to highlight recurrent patterns of brain activation, pooling information across subjects presents non-trivial methodological problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal activity and propose a methodological framework for statistical inference from a sample of EEG readings. Our work builds on classical contributions in time-series, clustering and functional data analysis, in an effort to reframe a challenging inferential problem in the context of familiar analytical techniques. Some attention is paid to computational issues, with a proposal based on the combination of machine learning and Bayesian techniques.